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@article{Satchwell:2022:10.1136/bmjopen-2022-067140,
author = {Satchwell, L and Wedlake, L and Greenlay, E and Li, X and Messiou, C and Glocker, B and Barwick, T and Barfoot, T and Doran, S and Leach, MO and Koh, DM and Kaiser, M and Winzeck, S and Qaiser, T and Aboagye, E and Rockall, A},
doi = {10.1136/bmjopen-2022-067140},
journal = {BMJ Open},
pages = {1--9},
title = {Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study},
url = {http://dx.doi.org/10.1136/bmjopen-2022-067140},
volume = {12},
year = {2022}
}

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TY  - JOUR
AB  - Introduction Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods.Methods and analysis This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment (‘reference standard’). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response.Ethics and dissemination MALIMAR has ethical approval from South Central—Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informe
AU  - Satchwell,L
AU  - Wedlake,L
AU  - Greenlay,E
AU  - Li,X
AU  - Messiou,C
AU  - Glocker,B
AU  - Barwick,T
AU  - Barfoot,T
AU  - Doran,S
AU  - Leach,MO
AU  - Koh,DM
AU  - Kaiser,M
AU  - Winzeck,S
AU  - Qaiser,T
AU  - Aboagye,E
AU  - Rockall,A
DO  - 10.1136/bmjopen-2022-067140
EP  - 9
PY  - 2022///
SN  - 2044-6055
SP  - 1
TI  - Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study
T2  - BMJ Open
UR  - http://dx.doi.org/10.1136/bmjopen-2022-067140
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000865504000031&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://bmjopen.bmj.com/content/12/10/e067140
UR  - http://hdl.handle.net/10044/1/101523
VL  - 12
ER  -

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